The present disclosure relates to systems and methods for magnetic resonance imaging (“MRI”). More particularly, the disclosure relates to systems and methods for controlling undesired magnetic field effects when performing an MRI study.
Iron is a naturally-occurring element in the human body. Iron is an essential nutrient for the human body; however, excessive iron is toxic, and the body has very limited capabilities to eliminate abnormal accumulation of iron. Iron overload can result in multiple complications.
For example, “transfusional” iron overload can be experienced by patients that require regular blood transfusions. Treatment for transfusional iron overload is based on administering iron-reducing “chelator” agents (chemicals that bind to excess iron and remove it from the body), either orally or intravenously. Treatment with chelators is extremely expensive and can exceed $40,000 per year and is potentially toxic. Therefore, in even this particular patient base, accurate measurement of body iron levels is critical to determine when to initiate treatment. It is also important to monitor treatment, allowing the adjustment of chelator dose to maintain low iron levels while minimizing risks from the treatment.
Current MRI-based iron quantification methods fall into one of two categories: quantitative susceptibility mapping (QSM) and relaxometry (e.g., measuring T2*) techniques. QSM is an MRI technique that provides a quantitative measure of tissue magnetic susceptibility and, thereby, iron concentration. Though relaxometry methods can also be used to assess iron concentration, in theory, QSM provides greater accuracy because it relies on direct measurement, rather than relaxometry measurements that are then correlated to iron quantification. However, in practice, the accuracy of QSM measurements can be compromised by a variety of confounding factors.
For example, magnetic fields from surrounding tissue or other sources can present “background fields” that interfere with the accurate assessment of tissue susceptibility in a given region of interest (ROI). The physical origin of the background field includes field inhomogeneity (imperfect shimming) in the B0 field, and susceptibility sources outside the ROI. For example, in brain imaging the magnetic field measured on frontal lobe may be particularly challenging because it includes fields induced by the frontal lobe (ROI), as well as fields induced by the skull and nasal cavity. Accordingly, if the magnetic field estimated from the MRI data includes information about fields induced outside the ROI (i.e., background fields), the QSM analysis may be influenced by these background fields. To overcome the confounding problems presented by background fields, various procedures referred to generally as background field removal have been developed.
Background field removal methods fall into two main categories: that include (1) algorithms based on harmonic function theory and (2) algorithms fitting the field with a series of basis functions. The first category of methods has fast performance relative to the second category, since the first can be implemented analytically, while the second must be implemented iteratively. One example of the former category includes sophisticated harmonic artifact reduction on phase data (SHARP), as described in Schweser, F., Deistung, A., Lehr, B. W., Reichenbach, J. R. (2011). “Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism?”. NeuroImage 54 (4): 2789-2807. An example of the latter category includes projection onto dipole fields (PDF), as described in Liu, T, Khalidov, I., de Rochefort, L., Spincemaille, P., et al. (2011), “A novel background field removal method for MRI using projection onto dipole fields”. NMR in Biomedicine 24 (9): 1129-36.
These methods have been evaluated for accuracy in the context of brain imaging. In the brain, structures of interest for susceptibility mapping to date have been small, located deep within tissue, far from air within sinuses, and have susceptibilities on the order of 0.3 ppm. This stands in contrast to ROIs in the abdomen, where the structures of interest include the liver, spleen, and pancreas. Abdominal structures have important differences from the brain. For example, the liver is large, has tissue adjoining air in the lungs, and can have susceptibility differences up to 9 ppm or more.
There continues to be a need for systems and methods to address background fields, particularly as the size of the ROI increases to accommodate larger structures, the structures are closer to an air interface, and the difference between a structure's susceptibility and the surrounding tissue's susceptibility is substantial.